The future of Data Science is Growing with the Advancement of AI and Machine Learning.

Data science in simple words means the study of data. It entails developing methods of recording, storing, and analyzing data to successfully bring out useful information. Data Science put together and make use of several statistical procedures. The procedures cover data modeling, data transformations, machine learning, statistical operations including descriptive and inferential statistics. For all data scientists statistics is the primary asset.

With the biggest innovation of the time, that is a cryptocurrency, the demands for controlling data online have become a crucial challenge. Various techniques are put forward by Data Science to identify a group of people and providing them with the best possible security from fraud activities.

However, the application of data science is not just concerned with one field rather its application disseminated across various sectors.

Major Future Developments in Data Science-

Healthcare sector- The biggest application of Data Science is in healthcare. The accessibility of large datasets of patients can be used to build a Data Science approach to identify the diseases at a very early stage. Healthcare is one of the biggest sectors for providing opportunities for the professional who can use their medical expertise with Data Science and provide immediate help to the suffering patients.

Arms and Weapons- Data Science can help in building various automated solutions to identify any attack at a very early stage. Other than that Data Science can help in constructing automated weapons that will be smart enough to identify when to fire and when not to.

Banking and Finance- Data Science in the Banking and Finance sector can be used in managing the money effectively to invest in the right places based on Data Science predictions for best results.

Other than the above sectors Data Science is also applied in Automobile Industry like self-driving cars, Fixed destination cabs as well as in Power and Energy. Data Science can predict the maximum safest potential and can help in building AI bots that can easily handle enormous power sources.

The implementation of Data Science cannot be ignored as it is already in action in the present stage. When you look for something in Myntra or Flipkart and then you get similar recommendations or similar advertisements for whatever you have searched on the internet is all about Data Science. The whole world is operated by Data Science. For every single search in Google, the process of data science is activated.

The future of data science is growing. According to Cloud Vendor Domo even when a person accounts for the Earth’s entire population, the average person is expected to generate 1.7 megabytes of data per second by the end of 2020.

An overreaching motif today and moving ahead, big data is assured to play an authoritative role in the future. Data will stipulate modern health care, finance, business management, marketing, government, energy, and manufacturing. The scale of big data is truly staggering as it has already entwined itself in the fundamental aspect of business as well as personal life.

The Dominance of AI and Machine Learning in near Future of Data Science-

Like almost all businesses prime concern is tech, there is a high possibility of the growth of data science jobs.

Artificial Intelligence is the most impactful technology among others that data scientists will run up into. Today Ai is already refining the business operations and assures to be a major trend in the near future. The applications of AI in today’s world have driven the adoption of other AI applications such as machine learning, deep learning and this will lead the way as the future of data science. Machine learning is the aptitude of statistical models to develop the capabilities and improve the performance with time in the absence of programmed instructions. This principle can be seen in the chess machine that is developed by Google’s DeepMind unit – the AlphaZero. The AlphaZero improves on its other computerized chess-playing peers in the absence of instructions is an example of how it learns from its movements to reach the most desired outcome.

As a greater number of businesses are merging with AI and data-based technologies at a high rate there is a need for a greater number of data scientists to help guide the initiatives.

Data science is a leviathan pool of multiple data operations that include statistics and machine learning. Machine Learning algorithms are very much dependent on data. Therefore, machine learning is the primary contributor to the future of data science. In particular data science covers the areas like Data Integration, Distributed Architecture, Automating Machine learning, Data Visualisation, Dashboards and BI, Data Engineering, Deployment in production mode, Automated, data-driven decisions.

While IT-focused jobs have been all the rage over the last two decades the rate of growth in the sector has been projected to be about 13% by the Bureau of Labor Statistics. It is still higher than the average rate of growth for all other sectors. However, data science has seen an explosive growth of over 650% since 2012 based on an analysis done on LinkedIn. The role of a Data Scientist has projected forward to one of the most in-demand jobs and ranks second to machine learning engineer- which is a job that is adjacent to a data scientist.

In the upcoming time, Data Scientists will have the ability to take on areas that are business-critical as well as several complex challenges. This will facilitate the businesses to make exponential leaps in the future. Companies in the present are facing a huge shortage of data scientists. However, this is set to change in the future.

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Gary Fowler is a serial AI entrepreneur with 17 startups and an IPO. He is CEO and co-founder of GSDVS.com and Yva.ai.

The long-standing question of whether technology will replace humans is a very popular topic for discussion.

And yet, the resolution to this debate is not that simple or black and white; there are a lot of factors playing into the relationship between technological process and human engagement.

Among the biggest trends in technological advancement that have been raising such questions are automation and hyperautomation. The two are different yet interrelated. And while the goal of each is, ultimately, improving and standardizing processes in the most efficient way possible, they represent two types of approaches to deploying machines to streamline processes.

Before explaining the relationship between automation and hyperautomation, let’s explore the meaning of hyperautomation first.

Hyperautomation

In its annual report on Top 10 Strategic Technology Trends for 2020, Gartner named hyperautomation the first trend that would transform the world. Accordion to UIPath, hyperautomation is the process of applying advanced technologies, such as artificial intelligence, machine learning and robotic process automation, to automate and templatize tasks that used to be humans’ responsibility. The integration of such advanced tech tools and technologies naturally amplifies our ability to automate work. 

Hyperautomation doesn’t stop there, though; it also includes the level and sophistication of automation. This process begins with robotic process automation (RPA) at the very base — which is the core of automation at its simplest — and broadens the horizons of automation through AI and machine learning.

In other words, hyperautomation builds on automation and broadens the meaning, goals and capabilities of automation, turning it into an ever-improving, AI-driven process that feeds on data. This leads to more accurate, faster and more efficient results.

The Difference

Going back to the question about the difference between automation and hyperautomation, the answer is very simple: at the core of hyperautomation is automation. But hyperautomation makes the end-to-end automation process more sophisticated, smarter and driven by AI-powered robotics. It becomes not just about the execution of tasks, but the optimization of the best ways to complete them.

The enhanced intelligence aspect of hyperautomation comes in many shapes and forms that seem very natural and ubiquitous nowadays. It can be an NLP algorithm that understands speech and writing and allows it to interpret communication. It can be the process of transforming images into text through optical character recognition — or OCR. Or it can be a machine learning algorithm that continuously analyzes data and identifies patterns to make more accurate predictions. At the end of the day, all these types of advanced technologies join forces to significantly increase the scope of automation possibilities.

Will Automation Replace Humans?

Going back to the highly debated question of whether automation will eventually replace humans, the answer is the following: the mission of automation has never been to replace humans. In fact, the goal of this trend is the exact opposite: it is to enable humans to fulfill their potential by focusing on high-involvement tasks and augment human capabilities to produce high-quality and highly specialized work. Automation ensures that mundane, repetitive tasks are relegated to machines to cut costs and increase productivity, all while continuously ensuring a superior quality of work in terms of the output that humans create when they get the opportunity to focus their efforts on high-risk tasks.

The same applies to hyperautomation — but in this case, the goal of technology is to augment human capabilities by working side-by-side with them to deliver maximum efficiency. With hyperautomation, the answer to the debatable question becomes more ambiguous and requires more analysis and thought. While automation might be a natural fit for a company from the operational perspective and is far less complicated to implement with human employees, hyperautomation brings the question of whether the business is ready to adopt a smart, AI-driven automation process that will operate in assistance to — and on par with — the human counterparts. It will become increasingly important to truly understand where hyperautomation fits in the larger business and how ready the employees are to have it operate with them synergistically.

What is also important is the transition from automation to hyperautomation through the introduction of AI and a higher level of robotic intelligence. It is key to establishing a network of multiple isolated instances of automation that will work together to continuously smooth out and streamline processes across multiple tasks and devices at the same time. To achieve this, it’s crucial to first assess the business’s capabilities and level of digitization before starting to build this network of AI-driven automation across multiple technologies.

At the core of hyperautomation is the right combination of a variety of tools connected by superior AI intelligence. For any business, it’s a good idea to identify a number of initial tools to invest in to start building the hyperautomated processes internally. As a rule of thumb, these tools need to belong to a single system and communicate with each other with ease to ensure maximum efficiency and smooth operations from day one.

But what perhaps matters more is understanding where the employees of a business stand with automation and how they view this introduction of new AI-powered processes. Before taking the leap to start hyperautomating, it’s crucial to ensure a welcoming environment internally and educate employees of all skills and backgrounds about how these new technologies will augment their abilities and introduce more weight to the work they do day to day. 

One way or another, AI-driven hyperautomation is not in the distant future anymore. It’s here and now, and the best way to embrace it is to kick off internal assessments and begin the adoption process one step at a time.

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